Mobile Robotics has advanced to a stage where robotic vehicles are
beginning to navigate autonomously and make decisions about the
traversibility of obstacles based on rich sensor data and machine
learning and planning techniques. Unfortunately, these systems can
still fail, especially in circumstances on which they were not
trained. My goal is to create a technique which should improve robot
safety and reliability by allowing robots to detect important changes
in their environment. If a robot sees a given scene more than once and
there is a significant change, such as a human being present or a tree
falling, the robot should be able to detect the change and avoid it or
alert a human. I am investigating machine learning techniques to
perform change detection on a set of rich sensor data collected by a
mobile robot.